Literature DB >> 29136608

Variation of the Korotkoff Stethoscope Sounds During Blood Pressure Measurement: Analysis Using a Convolutional Neural Network.

Fan Pan, Peiyu He, Chengyu Liu, Taiyong Li, Alan Murray, Dingchang Zheng.   

Abstract

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heartbeats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1 s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as "Korotkoff." A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analyzed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analyzed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analyzed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at sixth beat below SBP) and DBP (98.1%, at sixth beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high-cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject.

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Year:  2017        PMID: 29136608     DOI: 10.1109/JBHI.2017.2703115

Source DB:  PubMed          Journal:  IEEE J Biomed Health Inform        ISSN: 2168-2194            Impact factor:   5.772


  4 in total

1.  Quantitative Assessment of Blood Pressure Measurement Accuracy and Variability from Visual Auscultation Method by Observers without Receiving Medical Training.

Authors:  Wenai Chen; Fei Chen; Yong Feng; Aiqing Chen; Dingchang Zheng
Journal:  Biomed Res Int       Date:  2017-12-20       Impact factor: 3.411

2.  Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: a hybrid neural network approach.

Authors:  Stephanie Baker; Wei Xiang; Ian Atkinson
Journal:  Sci Rep       Date:  2020-12-04       Impact factor: 4.379

3.  Deep learning-based robust automatic non-invasive measurement of blood pressure using Korotkoff sounds.

Authors:  Ji-Ho Chang; Il Doh
Journal:  Sci Rep       Date:  2021-12-03       Impact factor: 4.379

Review 4.  Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods.

Authors:  Wenting Lin; Sixiang Jia; Yiwen Chen; Hanning Shi; Jianqiang Zhao; Zhe Li; Yiteng Wu; Hangpan Jiang; Qi Zhang; Wei Wang; Yayu Chen; Chao Feng; Shudong Xia
Journal:  Front Cardiovasc Med       Date:  2022-08-26
  4 in total

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